Informatics Research

Electronic health record-based phenotyping has become a popular approach for identifying eligible subjects for research studies. The current EHR-based phenotyping approach is based on multiple iterations of sequential steps and the ability of humans to uncover hidden relationships within the data. We are developing computational, machine learning methods that automatically phenotype subjects given their electronic health records. The goal of this research is to use computational methods to identify cases and controls for research and significant risk factors for diseases and disorders, and to predict risk for specific clinical events.

Machine Learning for Adverse Drug Events

The goal of this study is to develop and refine algorithms for detecting and predicting adverse drug events (ADE) from clinical data using established probabilistic models. The algorithms will then be tested for early detection of ADE using epidemiologically defined populations within central Wisconsin and Marshfield Clinic's electronic health record.

Comparative Surveillance of Generic Drugs by Machine Learning

The goal of this project is to develop a surveillance system that compares generic to brand-name drug experience using machine learning (ML) to predict which patients may suffer from previously unknown adverse drug events (ADE) while taking generic medications.

Application Development in Research

In addition to supporting ongoing research, BIRC Infrastructure and Central Resource (ICR) is currently working on rewrites of a majority of the administrative software used throughout the Research Foundation.

This large-scale project will connect and collate data from across the Research Foundation into fewer applications and allow for quick views of project status. This undertaking will allow for outdated programs to be replaced with efficient and well-designed products that can be fully supported within the Research Foundation.

BIRC and ICR’s excellence in informatics development make us well-suited to this type of data connection, collation and streamlining, and process improvement work.